Improving the transfer learning performances in the classification of the automotive traffic roads signs

Anass Barodi; Abderrahim Bajit; Mohammed Benbrahim; Ahmed Tamtaoui

doi:10.1051/e3sconf/202123400064

All issues

Volume 234 (2021)

E3S Web Conf., 234 (2021) 00064

References

Open Access

Issue		E3S Web Conf. Volume 234, 2021 The International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES2020)


Article Number		00064
Number of page(s)		8
DOI		https://doi.org/10.1051/e3sconf/202123400064
Published online		02 February 2021

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A. Bajit, M. Nahid, A. Tamtaoui, and M. Benbrahim, ‘A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions’, Adv. sci. technol. eng. syst. j., Vol. 5, no. 2, pp. 225-234, 2020, doi: 10.25046/aj050229 [Google Scholar]
Abderrahim. Bajit, Mohammed. Najid, Ahmed. Tamtaoui, and Abdellah. Lassioui, ‘A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools’, Adv. sci. technol. eng. syst. j., Vol. 4, no. 4, 2019, doi: 10.25046/aj040428 [Google Scholar]
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A. Barodi, A. Bajit, M. Benbrahim and A. Tamtaoui "Applying Real-Time Object Shapes Detection To Automotive Traffic Roads Signs.," 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Morocco, Kenitra, 2020, pp. 1-6, --Proceeding [Google Scholar]

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[1] J. Stilgoe, ‘Machine learning, social learning and the governance of self-driving cars’, Social Studies of Science, p. 32 [Google Scholar]

[2] A. Barodi, A. Bajit, M. Benbrahim, and A. Tamtaoui, ‘An Enhanced Approach in Detecting Object Applied to Automotive Traffic Roads Signs’, in 2020 IEEE 6th International Conference on Optimization and Applications (ICOA), Beni Mellal, Morocco, Apr. 2020, pp. 1-6, doi: 10.1109/ICOA49421.2020.9094457 [Google Scholar]

[3] S. Shi, Q. Wang, P. Xu, and X. Chu, ‘Benchmarking State-of-the-Art Deep Learning Software Tools’, in 2016 7th International Conference on Cloud Computing and Big Data (CCBD), Macau, China, Nov. 2016, pp. 99-104, doi: 10.1109/CCBD.2016.029 [Google Scholar]

[4] J. Chi, E. Walia, P. Babyn, J. Wang, G. Groot, and M. Eramian, ‘Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network’, J Digit Imaging, Vol. 30, no. 4, pp. 477-486, Aug. 2017, doi: 10.1007/s10278-017-9997-y [Google Scholar]

[5] G. Liang and L. Zheng, ‘A transfer learning method with deep residual network for pediatric pneumonia diagnosis’, Computer Methods and Programs in Biomedicine, Vol. 187, p. 104964, Apr. 2020, doi: 10.1016/j.cmpb.2019.06.023 [Google Scholar]

[6] K. Gopalakrishnan, S.K. Khaitan, A. Choudhary, and A. Agrawal, ‘Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection’, Construction and Building Materials, Vol. 157, pp. 322-330, Dec. 2017, doi: 10.1016/j.conbuildmat.2017.09.110 [Google Scholar]

[7] A. Paszke et al., ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’, p. 12 [Google Scholar]

[8] A. Bajit, M. Nahid, A. Tamtaoui, and M. Benbrahim, ‘A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions’, Adv. sci. technol. eng. syst. j., Vol. 5, no. 2, pp. 225-234, 2020, doi: 10.25046/aj050229 [Google Scholar]

[9] Abderrahim. Bajit, Mohammed. Najid, Ahmed. Tamtaoui, and Abdellah. Lassioui, ‘A Perceptually Optimized Embedded Image Coder and Quality Assessor Based Both on Visual Tools’, Adv. sci. technol. eng. syst. j., Vol. 4, no. 4, 2019, doi: 10.25046/aj040428 [Google Scholar]

[10] S. De, A. Mukherjee, and E. Ullah, ‘Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration’, arXiv:1807.06766 [cs, math, stat], Nov. 2018, Accessed: Aug. 08, 2020. [Online] [Google Scholar]

[11] M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, ‘Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network’, IEEE Trans. Med. Imaging, Vol. 35, no. 5, pp. 1207-1216, May 2016, doi: 10.1109/TMI.2016.2535865 [Google Scholar]

[12] A.W. Senior et al., ‘Improved protein structure prediction using potentials from deep learning’, Nature, Vol. 577, no. 7792, pp. 706-710, Jan. 2020, doi: 10.1038/s41586-019-1923-7 [Google Scholar]

[13] D.P. Kingma and J. Ba, ‘Adam: A Method for Stochastic Optimization’, arXiv:1412.6980 [cs], Jan. 2017, Accessed: Aug. 08, 2020. [Online]. Available: http://arxiv.org/abs/1412.6980 [Google Scholar]

[14] G. Pandey, A. Baranwal, and A. Semenov, ‘Identifying Images with Ladders Using Deep CNN Transfer Learning’, in Intelligent Decision Technologies 2019, Vol. 142, I. Czarnowski, R.J. Howlett, and L.C. Jain, Eds. Singapore: Springer Singapore, 2020, pp. 143-153 [Google Scholar]

[15] K. He, X. Zhang, S. Ren, and J. Sun, ‘Deep Residual Learning for Image Recognition’, arXiv:1512.03385 [cs], Dec. 2015, Accessed: Aug. 08, 2020. [Online]. Available: http://arxiv.org/abs/1512.03385 [Google Scholar]

[16] D. Li and Q. Chen, ‘Deep Reinforced Attention Learning for Quality-Aware Visual Recognition’, arXiv:2007.06156 [cs], Jul. 2020, Accessed: Aug. 08, 2020 [Google Scholar]

[17] J. Cohen, ‘A Coefficient of Agreement for Nominal Scales’, Educational and Psychological Measurement, Vol. 20, no. 1, pp. 37-46, Apr. 1960, doi: 10.1177/001316446002000104 [Google Scholar]

[18] A. Barodi, A. Bajit, M. Benbrahim and A. Tamtaoui "Applying Real-Time Object Shapes Detection To Automotive Traffic Roads Signs.," 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Morocco, Kenitra, 2020, pp. 1-6, --Proceeding [Google Scholar]